| After years of research and development of complex networks,models for exploring network characteristics are also advancing with the times.However,with the deepening of modern network research and the advent of the era of big data,the scale of the actual network continues to expand.It has become an important research direction to study the community structure of large-scale network networks and how to reduce the network size.Discovery is a hot research area at the moment.However,there are few studies on coarse graining algorithms that focus on maintaining the network synchronization ability.In addition,the algorithms discovered by the community usually cost a lot of computing power and time.Therefore,this paper studies the coarse graining algorithm and community detection algorithm based on h-index.The main contents and innovations of the full text are as follows: After years of research and development of complex networks,models for exploring network characteristics are also advancing with the times.However,with the deepening of modern network research and the advent of the era of big data,the scale of the actual network continues to expand.It has become an important research direction to study the community structure of large-scale network networks and how to reduce the network size.Discovery is a hot research area at the moment.However,there are few studies on coarse graining algorithms that focus on maintaining the network synchronization ability.In addition,the algorithms discovered by the community usually cost a lot of computing power and time.Therefore,this paper studies the coarse graining algorithm and community detection algorithm based on h-index.The main contents and innovations of the full text are as follows:(1)The h-index was proposed as an academic evaluation index.In the past,it was used to measure the citation impact of scholars or journals.By extending it to the network,it can be used to describe the importance of nodes.There are few previous studies.The h-index is applied in the coarse graining process of the network.In this paper,a coarse graining algorithm(called HVCCG)of the network is constructed based on h-index.The research shows that the coarse graining algorithm based on h-index can keep the average degree and synchronization ability of coarse graining network at the same time to a certain extent.Compared with the previous coarse graining methods,the HVCCG method only needs the connectivity level of the network,which not only requires less computation,but also maintains some statistical features and synchronization capabilities of the original network.Furthermore,the new algorithm is free to choose any size of coarse graining network.It provides new ideas for the study of large-scale networks.(2)High-order h-index can be obtained by using h-index iteration,and high-order hindex contains more information of neighbor nodes,which makes h-index can be applied to network community division.In the process of community division of the network.In this paper,a community detection algorithm based on h-index(named as HCD)is developed,and its modular community structure is parsed from the complex network by using the information contained in the graph topology.The algorithm has the characteristics of low time cost and no need to specify the number of communities.This paper also extends HCD to overlapping community detection and applies it to address dynamic Covid-19 networks.Finally,the algorithm is verified by simulation experiments,and the results show that the algorithm developed in this paper has certain validity and feasibility. |
